Dynamic background subtraction via sparse representation of dynamic textures in a low-dimensional subspace

In this paper, we deal with the problem of background subtraction especially for the scenes containing dynamic textures. In the scenes, unlike static textures, dynamic textures show a wide range of per-pixel color variations over time. For successful dynamic background subtraction, therefore, it is an essential task to represent the dynamics of these variations effectively. For this task, in the proposed method, i) a training set of dynamic background scenes is modeled in a low-dimensional subspace and then ii) the background of a test scene is represented as a linear combination of a few coefficient matrices resulting from the projection of the training scenes onto the low-dimensional subspace. More specifically, the proposed dynamic background subtraction method is based on the sparse representation of dynamic textures in the low-dimensional subspace. In the experiments, the proposed method shows promising performance in comparison with other competitive methods in the literature.

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